COVID-19: Estimates suggest COVID-19 transmission rates are highly seasonal
COVID-19: Estimates suggest COVID-19 transmission rates are highly seasonal. Spencer EA, Heneghan C.
Published on June 27, 2020
Transmission Dynamics of COVID-19
||Carleton T, Meng KC. Causal empirical estimates suggest COVID-19 transmission rates are highly seasonal medRxiv 2020
||Temperature, Humidity, Population density
Seasonal temperature is associated with COVID-19 transmission globally, with 1°C increase in local temperature associated with 13% fewer cases.
Adjusting for precipitation, humidity, population density and public health intervention, the association between weather temperature and COVID-19 cases was estimated as:
each 1C increase in local temperature was associated with 13%, fewer COVID-19 cases per 1 million people by (95% CI -4% to -21%).
This result was robust to controlling for precipitation and specific humidity, neither of which exhibited statistically significant effects (although point estimates on humidity were negative, consistent with prior evidence for influenza).
What did they do?
The study estimated the relationship between local temperature and COVID-19 transmission, using a global sample of 166,686 confirmed new COVID-19 cases from 134 countries from 22nd January 2020 to 15th March 2020, aiming to control for local public health interventions, surrogate for UV exposure, and population densities.
Data on the following were collected: daily temperature, precipitation, specific humidity for every 0.25◦ latitude by 0.25◦ longitude pixel of the planet, generated by a climate reanalysis model in near real-time.
Using population weights pixel-level weather variables were aggregated to country-level reports of COVID-19 cases. A global function between temperature and new cases of COVID-19 per 1 million people was estimated.
The study used the ERA5 reanalysis product from the European Centre for Medium-Range Weather Forecasts: https://cds.climate.copernicus.eu/cdsapp#!/home: The ERA5 is freely available and functions as a one-stop-shop to explore climate data.
This is a large sample and the methods aim to avoid major confounding by environmental and socioeconomic influences.
Only confirmed cases were analysed which may under-estimate the magnitude of the link between infection and local climatic conditions. Countries around the world also have different testing capacity, making under-reporting heterogeneous and estimates prone to bias.
|Clearly defined setting
||Demographic characteristics described
||Follow-up length was sufficient
||Transmission outcomes assessed
||Main biases are taken into consideration
What else should I consider?
Temperature is correlated with many often unobservable potential confounding factors. Cross-sectional comparisons may not have a causal interpretation. e.g., countries that are cooler on average also tend to have higher income per capita which may affect the number of new COVID-19 cases by enabling more testing and hospitalizations.
About the authors